Predictive network system and method

ABSTRACT

A proactive networking system and method is disclosed. The network anticipates the user demands in advance and utilizes this predictive ability to reduce the peak to average ratio of the wireless traffic and yield significant savings in the required resources to guarantee certain Quality of Service (QoS) metrics. The system and method focuses on the existing cellular architecture and involves the design and analysis of learning algorithms, predictive resource allocation strategies, and incentive techniques to maximize the efficiency of proactive cellular networks. The system and method further involve proactive peer-to-peer (P2P) overlaying, which leverages the spatial and social structure of the network. Machine learning techniques are applied to find the optimal tradeoff between predictions that result in content being retrieved that the user ultimately never requests, and requests that are not anticipated in a timely manner.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/251,761, filed on Jan. 18, 2019, which is a continuation of U.S.patent application Ser. No. 15/618,995, filed on Jun. 9, 2017, issuingas U.S. Pat. No. 10,187,327, which is a continuation of U.S. patentapplication Ser. No. 13/876,781, filed Jan. 2, 2014, issuing as U.S.Pat. No. 9,680,766 on Jun. 13, 2017, which is a national stage entry ofPCT/US11/53746, filed Sep. 28, 2011, which claims priority to U.S.Provisional Patent Application No. 61/387,285 filed on Sep. 28, 2010.Each of which is incorporated by reference as if fully recited herein.

FIELD OF THE INVENTION

The present invention relates to network traffic management. Inparticular, the present invention relates to a system and method forresource allocation in wireless or wired networks.

BACKGROUND OF THE INVENTION

The wireless spectrum is a limited and nonrenewable resource which isincreasingly challenged by the growing demand for service created by thewidespread availability of smart devices connecting to the Internetthrough cellular networks. Estimates are that the rate of data trafficover cellular networks will increase 40-fold in the next five years. Itis expected that, by 2015 there will be a projected 158 million users inthe US accessing the Internet via a wireless mobile device, and 68.5% ofthe traffic will be generated by mobile video. According to a report onmobile access from the Pew Internet and American Life Project, 59% ofall adults in the US today access the internet through a wirelessconnection, and 40% use a mobile phone to access the Web, email, orinstant messaging [1]. To meet the growing load, a system and method ofproactive networking is disclosed.

SUMMARY OF THE INVENTION

In the disclosed proactive networking paradigm, the network anticipatesthe user demands in advance and utilizes this predictive ability toreduce the peak to average ratio of the wireless traffic, and hence,yield significant savings in the required resources to guarantee certainQuality of Service (QoS) metrics. The system and method focuses on theexisting cellular architecture and involves the design and analysis oflearning algorithms, predictive resource allocation strategies, andincentive techniques to maximize the efficiency of proactive cellularnetworks. The system and method further involve proactive peer-to-peer(P2P) overlaying, which leverages the spatial and social structure ofthe network. The system and method may used in any data trafficmanagement network, whether wireless or wired.

A systematic framework for the design and analysis of proactive networksis disclosed. The network may be wireless or wired. Machine learningtechniques are applied to find the optimal tradeoff between predictionsthat result in content being retrieved that the user ultimately neverrequests, and requests that are not anticipated in a timely manner. Thedesign of proactive resource allocation mechanisms, multi-castingtechniques, pricing policies, and gossip algorithms enhances the theoryof network optimization and control. The design of efficientdevice-to-device communication algorithms yields new fundamental resultsin network information theory.

The Pew report found that African-Americans and Latinos employ many moreof the data functions of their mobile phones than Caucasian cell phoneowners[1]. Thus traditional solutions to the spectrum crunch which relyon economic disincentives for use may have the effect ofdisproportionately shifting the burden to underserved populations.Solutions like the ones proposed herein are needed in order to preserveinexpensive access for all, not only to the informational resources ofthe Internet but to newer and throughput-hungry applications whichbenefit consumers.

Over the last few years, there has been an ever increasing demand forwireless spectrum resulting from the adoption of throughput hungryapplications in a variety of civilian, military, and scientificsettings. In particular, it is widely recognized that multimediadownload generated by Internet-capable smart phones and other portabledevices (e.g., iPad) strains cellular wireless networks to a degreewhere service quality for all users is significantly impacted. Becausethe available spectrum is limited and non-renewable, this demand poses aserious challenge leading wireless operators around the world toconsider significant additional investments in the cellularinfrastructure in the form of more base stations towers and thecorresponding re-planning of the cellular coverage to guarantee anacceptable quality of service for these new high throughput services.

In the disclosed proactive networking paradigm, the network anticipatesthe user demands in advance and utilizes this predictive ability toachieve significant savings in the required resources to guaranteecertain Quality of Service (QoS) metrics via judicial matching betweensupply and demand. This paradigm meets the demands of broadband wirelessnetworking and other networking demands.

While there is a severe shortage in the spectrum, it is well known thata significant fraction of the available spectrum is under-utilized.This, in fact, is the main motivation for the cognitive networkingframework where secondary users are allowed to use the spectrum in theoff time, where the primary users are idle, in an attempt to maximizethe spectral efficiency. Unfortunately, the cognitive radio approach isstill facing significant regulatory and technological hurdles and, atbest, offers only a partial solution to the problem. This limitation ofthe cognitive radio approach is intimately tied to the main reasonbehind the under-utilization of the spectrum; namely the large disparitybetween the average and peak traffic demand in the network. As anexample, in a typical cellular network, one can easily see that thetraffic demand in the peak hours is much higher than that at night;which inspires the different rates offered by cellular operators. Now,the cognitive radio approach assumes that the secondary users are beable to utilize the spectrum in the off peak times but, unfortunately,at those particular times one may expect the secondary trafficcharacteristics to be similar to that of the primary users (e.g., atnight most of the primary and secondary users are expected to be idle).The proactive resource allocation framework avoids this limitation, andhence, achieves a significant reduction in the peak to average demandratio without relying on out-of-network users.

In the traditional approach, wireless networks are constructed assumingthat the subscribers are equipped with dumb terminals with very limitedcomputational power. It is obvious that the new generation of smartdevices enjoy significantly enhanced capabilities in terms of bothprocessing power and available memory. Moreover, according to Moore'slaw predictions, one should expect the computational and memoryresources available at the typical wireless device to increase at anexponential rate. This observation should inspire a similar paradigmshift in the design of wireless networks whereby the capabilities of thesmart wireless terminals are leveraged to maximize the utility of thefrequency spectrum, a non renewable resource that does not scaleaccording to Moore's law. The disclosed proactive resource allocationframework is a significant step in this direction.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a plot of outage probability vs. C log C with λ=0.8.

FIG. 2 is a block diagram of a predictive resource allocationconfiguration for cellular networks.

FIG. 3 is a plot of system performance for varying β and μ*.

DETAILED DESCRIPTION

The introduction of smart phones, most notably the iPhone, has resultedin a paradigm shift in the dominant traffic in mobile cellular networks.Whereas the primary traffic source in traditional cellular networks wasreal-time voice communication, one can argue that a significant fractionof the traffic generated by the smart phones results from non-real-timedata requests (e.g., file downloads). As demonstrated in the following,this feature allows for more degrees of freedom in the design of thescheduling algorithm.

The usage of the wireless devices is highly predictable. This claim issupported by a growing body of evidence that range from the recentlaunch of Google Instant to the interesting findings on predictablemobility patterns. In one context, a relevant example would be the factthat user's preference for a particular news outlet is not expected tochange frequently. So, if the smart phone observes that the user isdownloading CNN, for example, in the morning for a sequence of days in arow then it can safely anticipate that the user will be interested inthe CNN again the following day. Coupled with the fact that the mostwebsites are refreshed at a relatively slow rate, as compared with thedynamics of the underlying wireless network, one can now see thepotential for scheduling early downloads of the predictable traffic toreduce the peak to average traffic demand by maximally exploiting theavailable spectrum in the network idle time. When a proactive networkserves a request before its deadline, the corresponding data is storedin the user device and, when the request is actually initiated, theapplication pulls the information directly from the memory instead ofaccessing the wireless network.

In existing cellular networks, each traffic request is consideredurgent, at the time scale of the application layer, and hence, must beserved upon initiation or dropped resulting in an outage or blockingevent. However, if the user device can anticipate the requests to begenerated by its corresponding user and submit them in advance, then thenetwork will have the flexibility in scheduling these requests over anexpanded time horizon as long as the imposed deadlines are not violated.To demonstrate the potential gains of such predictive operation,consider a very simplified single cell model where time is divided intoslots and the requests are allowed to arrive only at the beginning ofeach slot. The aggregate number of arriving requests at time slot n>0 isdenoted by Q(n) which is assumed to follow a Poisson distribution withrate λ. All requests are assumed to have the same amount of requiredresources which is taken to be unity. That is, each request has to betotally served in a single slot by allocating one unit of resources.Moreover, the wireless network has a fixed capacity C (total resources)per slot, i.e., the number of served requests per slot cannot exceed C.Furthermore, assume that the proactive network can anticipate thearrival of each request by T time slots a-priori. Thus, if q(n),1≤q(n)≤Q(n), is the ID of a request predicted at the beginning of timeslot n, its deadline is D_(q(n))=n+T. The non-predictive networkcorresponds to the special case of T=0 implying that all arrivingrequests at the beginning of time slot n have to be served in the sametime slot n, i.e., D_(q(n))=n, or otherwise they are dropped. Finally,an outage event occurs at a certain time slot if and only if at leastone of the requests in the system expires in this slot.

The prediction diversity gain exhibits itself as the rate of decay ofthe outage probability in the asymptotic scenario where both the requestarrival rate λ and the network capacity C grow to infinity. To capturethe tradeoff between reliability, as measured by the outage probability,and throughput, as measured by the aggregate arrival rate, λ=C^(γ),0≤γ≤1 and define the diversity gain, as a function of γ, to be

${d(\gamma)}\overset{\Delta}{=}{\lim\limits_{c\rightarrow\infty}{\frac{{- \log}\mspace{11mu} P\mspace{11mu} ({outage})}{C\mspace{11mu} \log \mspace{11mu} C}.}}$

This formulation is akin to the diversity-multiplexing tradeoff used tocharacterize the fundamental asymptotic limits of multi-terminalcommunication over fading channels where γ plays the role of themultiplexing gain. The polynomial scaling of the aggregate arrival ratewith C and corresponding definition for the diversity gain aim only atsimplifying the analysis. In fact, a similar conclusion is derived infor the case of linear throughput scaling λ=γC, 0≤γ≤1 with diversitygain metric

${d_{Ltn}(\gamma)}\overset{\Delta}{=}{\lim\limits_{C\rightarrow\infty}{{- \left( {\log \mspace{11mu} {P({outage})}} \right)}/{C.}}}$

The following result establishes the prediction diversity gain obtainedthrough judicious proactive resource allocation in a simplified model.

Theorem 1. The diversity gain of proactive scheduling for the abovemodel with T-slot prediction equals

d _(p)(γ)=(1+T)(1−γ)

Noting that the diversity gain of the non-predictive scenario isobtained as a special case by setting T=0, i.e., d_(N)(γ)=(1−γ), thisresult reveals that proactive scheduling offers a multiplicative gain of(1+T) in the achievable diversity advantage.

Proof: Start with the non-predictive benchmark corresponding to T=0. Inthis case, the outage probability in any slot n corresponds to the event{Q(n)>C}, which can be expressed as

${P_{N}({outage})} = {\sum\limits_{k = {C + 1}}^{\infty}{\frac{\left( C^{\gamma} \right)^{k}}{k!}{e^{- C^{\gamma}}.}}}$

For large values of C, the above outage probability can be rewrittenusing Stirling's approximation as follows,

$\begin{matrix}{{P_{N}({outage})} = {\sum\limits_{k = {C + 1}}^{\infty}{\frac{1}{\sqrt{2\pi k}}\frac{\left( {C^{\gamma}e} \right)^{k}}{k^{k}}{e^{- C^{\gamma}}.}}}} & (1)\end{matrix}$

The denominator of the k^(th) term in the above summation scales ask^(k), hence, in the asymptotic scenario where C→∞ the dominant term in(1) is at k=C+1.

$\begin{matrix}{{d_{N}(\gamma)} = {{\lim\limits_{C\rightarrow\infty}\left\lbrack {{\frac{1}{2C\mspace{11mu} \log \mspace{11mu} C}{\log \left( {2{\pi \left( {C + 1} \right)}} \right)}} - {\gamma.\frac{C + 1}{C}} - \frac{C + 1}{C\mspace{11mu} \log \mspace{11mu} C} + {\frac{C + 1}{C}\frac{\log \mspace{11mu} \left( {C + 1} \right)}{\log \mspace{11mu} C}} + \frac{C^{\gamma}}{C\mspace{11mu} \log \mspace{11mu} C}} \right\rbrack} = {1 - {\gamma.}}}} & (2)\end{matrix}$

For T>0, it is easy to see that the First-In-First-Out (FIFO), orequivalently Earliest Deadline First (EDF), scheduling policy minimizesthe outage probability in this simple scenario. To characterize thediversity gain, the following two events to upper and lower bound theoutage event are defined.

${{_{d}(n)}\overset{\Delta}{=}\left\{ {{\sum\limits_{i = {n - {2T}}}^{n - T}{Q(i)}} > {C\left( {T + 1} \right)}} \right\}},{{\mathcal{L}_{d}(n)}\overset{\Delta}{=}{\left\{ {{Q\left( {n - T} \right)} > {C\left( {T + 1} \right)}} \right\}.}}$

In the steady state, i.e., when n→∞,

Pr(

_(d)(n))≤P _(p)(outage)≤Pr(U _(d)(n)).

Further

${{\lim\limits_{C\rightarrow\infty}{- \frac{\log \mspace{11mu} {\Pr \left( \mathcal{L}_{d} \right)}}{C\mspace{11mu} \log \mspace{11mu} C}}} = {{\lim\limits_{C\rightarrow\infty}{- \frac{\log \mspace{11mu} {\Pr \left( _{d} \right)}}{C\mspace{11mu} \log \mspace{11mu} C}}} = {\left( {1 + T} \right)\left( {1 - \gamma} \right)}}}.$

Combining these two relationships results in the diversity gainexpression

d _(p)(γ)=(1+T)(1−γ).

FIG. 1 reports a numerical example validating analytical results. Thesuperior diversity advantage of proactive scheduling is apparent in thesteeper slopes of the corresponding outage probability curves ascompared with the non-predictive benchmark. It is further shown that aremarkable gain is still possible when T follows a uniform distributionand is different from one request to the next. Overall, one can see thatproactive scheduling offers a significant reduction in the capacity Crequired to attain a certain outage probability.

A proactive networking framework rests on the notion that theinformation demand patterns of mobile and other device users arepredictable. Such predictability can then be exploited in opportunisticpre-fetch of desired information, thus achieving improved userexperience by reducing network outages, for a fixed spectrum bandwidth,or reducing the required bandwidth to achieve a certain outageprobability.

The data consumption patterns that are most relevant to the context ofinformation delivery to mobile devices are described. Key enablingtechniques for discovering these patterns automatically is alsodescribed. Two main classes of information demand patterns arepresented: Stationary User Behavior Patterns and Non-stationary UserBehavior Patterns.

Stationary User Behavior Patterns. In their simplest forms, users havehabits that can be easily captured and predicted. For example, beforeleaving the driveway, a user might check real-time traffic from a localtraffic monitoring site. During stop-and-go traffic, the user might scanemails in his Gmail account. More precisely, at any given time t and anylocation x, a user might access information stochastically and requestfrom different data resources on the web. Nevertheless, the probabilityof p(d|x, t) can be estimated with reasonable accuracy, where d∈D is adata resource such as a website's URL, by exploiting the user'shistorical usage data (such as website visit records). Furthermore, theaverage demand on the network associated with requesting informationfrom d can be estimated. This is an important quantity in optimizing thecapacity of predictive networks. Note that the widespread availabilityof GPS service on smart wireless devices allows for exploiting locationinformation to enhance the performance of prediction algorithms.

Non-stationary User Behavior Patterns. Users also interact with dataresources. The implication is that data consumption patterns varynon-stationarily. In the Gmail example above, mobile devices oftendisplay headings (and the first few lines) of emails. If the user isinterested in reading a particular email, the rest of the email body isthen downloaded. Similarly, for CNN.com, the user might choose to followup a news headline by reading the full story. There is a need not onlyto estimate p(d|x, t) but also to predict whether the user would beinterested in data resources implied by d at time t+1. Specifically, dis modeled as a collection of links to other data resources d¹, d², . .. , d^(N). As soon as d is delivered to the mobile device, these linksbecome visible and the mobile device then estimates p(d^(n)|x, t+1)based on the user's profile (what type of news/data interest him),historical records, etc.

There has been a considerable amount of work on the personalization ofthe user web experience. However, work on personalization for mobilecontent delivery is scarce. In particular, while motivated byopportunistic content pre-fetch, these works focus on batch contentdelivery (of a whole site) to mobile devices; therefore, a granularitymuch bigger than what is relevant for mobile devices. Note that smallergranularity enables much more intelligent use of communication capacity,especially if the goal is to support tens of millions of mobilesubscribers. Statistical learning techniques may be used to predictusers' data usage patterns.

In an example embodiment, an intelligent agent (a piece of software)runs on a mobile device. Alternatively, the intelligent agent mayexecute at a server. The agent logs user data usage, including the URLsof visited websites/data resources, click events (to follow up on anemail or a news story), and other actions reflecting the userinteraction with dynamically delivered data. Based on these historicalrecords, statistical models of users' preference and likelihood offetching contents from different data resources are built. Data sparsityis a key challenge this research plans to address. It may not bepossible to collect enough data from a single user in order to predicther patterns precisely enough. To overcome this challenge, other users'data is leveraged to build statistical models. To start, groups sharingsimilar interests or web browsing patterns are clustered. Also clusteredare data resources based on their similarity in topics or genres. Thenmodels for preferences between user groups and data resource clustersare built. This approach enables access to a much larger collection ofdata in order to make better prediction of the user's intent. Avoidingtunnel vision is another challenge addressed in the predictionframework. Concretely, imagine that there is a major political event ornatural disaster. Such events happen rarely and it is unlikelyhistorical data will be sufficient for models to learn well.Consequently, models will likely rate news about this event as unlikelyto be noticed by users, yet many users could be still interested in iteven though they have never explicitly expressed interest before. Toaddress this problem, models are built for domain knowledge (whatinterests the population in general), using techniques fornovelty/anomaly detection.

Action: Being able to predict mobile users' data consumption patternsprovides a significant advantage in designing content pre-fetch schemesso as to reduce average traffic load on the network and network outageprobabilities. Concretely, pre-fetching data from sourced at any timet<T if p(d|x,T) is sufficiently high. However, dynamics of user demandsare also taken into consideration. Specifically, a “look-ahead” strategyis used to: i) determine an optimal time t≤t_(d)≤T to issue a request tothe network; ii) determine an optimal expiration time e_(d)≤T of therequest; iii) assuming the request is fulfilled at time(t_(d)+1)≤r_(d)≤e_(d), analyze the information from d and sendingrequests to data sources d′ revealed by d (and predicted as being highlylikely to be followed up by the user), with expiration time (T+1).Obviously, this process can be nested in many levels, to continuepursuing opportunistic pre-fetch of information on d′.

Optimization algorithms may optimally specify (t_(d), e_(d)) for a datasource d. The problem may be cast as a decision problem based on Markovdecision process (MDP). At any time t, a decision agent maintains a listof data sources and their expiration times. The decision agent canchoose from a set of actions, including idle (do nothing), selecting oneor a few data sources and submit to the network and specify thecorresponding expiration time. In other words, specifying (t_(d), e_(d))is partially reduced to the decision problem of whether to submit d attime t. As indicated previously, the agent may execute on the mobiledevice or at a server.

If a d is chosen to be submitted at time t, the decision agent suffers apenalty in the amount of (e_(d)−T_(d)), where T_(d) is the organicexpiration time of d. Namely, the agent is penalized for being overlyzealous to get data too early. Additionally, if a previous request d′ isfulfilled, the agent receives a reward p(d′), i.e., the likelihood auser will examine data on d′ (estimated by the predictive models). Thecentral challenge is to learn such a policy that maximizes the agent'stotal rewards. The reward-penalty mechanism forces the agent to adopt apolicy that: i) strategizes which d to select as the right d also bringsopportunities of more rewards if d contains a lot of links (to otherdata) that could interest the user; ii) does not abuse the network bysubmitting requests with myopic expiration time so as to exceed networkcapacity. Computing the optimal policy is a well-studied problem inreinforcement learning. The techniques have been applied to problems incomputer systems and networks, such as resource allocation, routing andpower management. Their utility, however, has not been examined in thecontext of scheduling data requests on mobile devices.

Prediction under Proaction: Contents pre-fetch does not have to beinitiated by passively matching well-structured users' behaviorpatterns. Proactive networks are capable of actively recommending anddelivering contents that shape users' interest. This is achieved bybuilding a content recommendation system that categorizes contents inmuch broader categories. A key notion is that, contents in a broadcategory are exchangeable with respect to users' general interests.These categories are then used to fulfill users' nonspecific datarequest, for instance, music specified in terms of genres instead of aspecific performing artist. This enables proactive networks to take afurther advantage of off-peak time traffic to preemptively delivercontents to user devices. Arguably, the predictor's task is easier as itonly needs to predict broader categories. On the other end, thepredictor will likely have less precise features to make a prediction.For example, in predicting whether a user is going to read the full bodyof an email on the mobile device, the sender of the email could be avery informative feature. Therefore, while contents categorization canoften be solved with unsupervised machine learning algorithms (such asprobabilistic latent semantic analysis or topic models), the constraintthat the resulting categories need to be well predictable with users'data should be considered. This problem is addressed by jointlyoptimizing the objective functions used for prediction and discoveringcategories.

Analysis: Constructing an analytical framework for characterizingprediction and action errors is an important overarching topic. Thereare two major classes of errors: 1) pre-fetching data that ended up notbeing accessed by the user; and 2) failing to predict some of the userrequests. The first class results in an increase in the average trafficload seen by the network, whereas the second leads to urgent requeststhat must be met instantaneously. Clearly, there is a tension betweenthose events since a more aggressive pre-fetching policy is expected todecrease the probability of the second event at the expense ofincreasing the probability of the first. A deep understanding of theunderlying fundamental tradeoff then guides the optimization of resourceallocation and scheduling policies.

Proactive Resource Allocation and Scheduling

Referring to FIG. 2, a Predictive Resource Allocation Setup for CellularNetworks diagram is shown. The basic setup is depicted in FIG. 2 withthe cellular network serving many users over fading channels, where eachuser runs a number of applications that demand data from dynamic orstatic sources.

Modeling of Data Sources and Download Costs: Data sources can be broadlycategorized into two classes: static and dynamic, depending on thevalidity of their information content. While the content of staticsources, such as stored videos, audio, books, etc., does not change overtime, the dynamic sources' content, such as news, weather conditions,podcasts, webcasts, etc., continuously changes over time with differentupdate characteristics. Due to the differences in the validity andavailability of their content, the modeling and operation of proactiveresource allocation differs for serving static and dynamic data sources.

The characteristics of static data sources may be captured by agraphical model where each content is modeled as nodes of an expandinggraph with links connecting related content. Then, based on the approachdiscussed above, the predictions of a particular user interest arecaptured through dynamically modified link weights that indicate theprobability with which a user may demand new content based on its pastchoices. The graph keeps expanding as new data arrives to the staticdata source, and new links to existing data are generated based on theobserved user interest in the new content.

Alternatively, for dynamic sources, the notion of the ‘type’ of data,such as sport news or weather updates, etc., that is requested isestablished, rather than specific everlasting data as in the staticcase. In this case, the application demands the most recent content ofthe data type rather than a particular content. Thus, the stochastic andtemporal characteristics of the update process for different data typesis modeled and utilized in the design. The following example reveals theimpact of source and user dynamics as well as transmission costs on theoptimum design.

In the context of FIG. 2, suppose a newscast application is being servedby the cellular network. The news content is updated as new informationarrives to the data source, modeled as a Poisson process with rate λupdates/minute, which is reasonable considering unrelated news arrivalas independent and memoryless. The user checks his application at randomtimes, also modeled as a Poisson process with rate β checks/minute. Anenergy cost of C is imposed to each download from the cell station tothe user to account for the transmission power consumption. Finally, thestation checks for updates and downloads new content according to aPoisson process with rate μ download/min. If the user holds the mostrecent update when a request arrives, it is called a success, otherwisethe request fails. Then, a reasonable goal is to find the optimum rateμ* at which the station should operate in order to maximize thedifference of the success rate from the total failure and cost.

FIG. 3 depicts the typical forms this objective function take as afunction of the server download rate μ for a constant update rate of λ=5and unit cost, and varying values of the request rates β. Note that thebehavior of the function depends on the request rate value, exhibiting aunimodal nature for small β and a monotone nature as β approaches acritical value. Using a Markov model of the system operation and renewaltheorem, the optimum rate μ* may be expressed explicitly in terms of λ,β, and C as follows:

$\mu^{*} = {\frac{\left( {\sqrt{C\beta^{2}{\lambda^{5}\left( {\lambda - \beta} \right)}} + {\beta {\lambda \left( {\beta^{2} + {C\beta \lambda} - \lambda^{2}} \right)}}} \right)}{\left( {\lambda^{3} + {\lambda^{2}\beta} - {\left( {1 + C} \right)\beta^{2}\lambda} - \beta^{3}} \right)}.}$

This function is plotted in the upper-right of FIG. 3 as a function of βfor λ=5, C=1, which clearly illustrates that the download rate sharplyincreases as β approaches the critical value around ≈4 when the abovedenominator vanishes. Intuitively, when β approaches this criticallevel, the request rate is high enough to enforce the station todownload the updates almost as soon as they arrive.

This example reveals the sensitivity of optimum rate allocation to theupdate and request dynamics. In the context of the disclosed paradigm,by adequately anticipating the user requests, the randomness in thedemand process can be significantly reduced allowing for more efficientdelivery with a much lower cost. However, in order to collect thesepotential gains, the proactive resource allocation policy is constructedwith such dynamics in mind. The following approaches may be employed:(i) modeling static sources through graphical modeling and learning;(ii) modeling of dynamic sources through probabilistic and informationtheoretic techniques; and (iii) developing the optimal predictiveresource allocation policies using stochastic control and optimizationmethods.

Incorporating Storage Limitations and QoS Heterogeneity: While theavailable storage space increases steadily at the mobile users, thenumber and demanded data size of mobile applications also increasesrapidly. Consequently, the amount of data that a user may potentially beinterested in grows accordingly, pushing the limits of available memoryof the smart phone technologies. Storage limitations may also be imposedby individual users due to privacy and security reasons. Such storagespace limitations call for a significant shift in the optimal proactiveresource allocation design paradigm. To see the impact of storagelimitations, consider a simple scenario where the download channelyields a high rate and the prediction of application demands is perfect.Yet, if the buffer space is limited, the content cannot be downloadedimmediately and must wait for the use of existing content before newcontent can be downloaded. Queueing theoretic tools may be employed tomodel these subtle dynamics in the setup of FIG. 2, initially for thesingle-user scenario, and then for multi-user scenario.

As a related item, it is also important to capture the varying QoSrequirements of applications together with fair distribution ofpredictive services across users/applications. In the recent years,there have been significant advances in the design of fair and efficientnetwork controllers for throughput maximization, and, more recently,under multi-timescale QoS constraints that capture real-time trafficrequirements. Yet, these results do not directly apply to proactiveresource allocation under buffer constraints since the predictiveoperation allows for more flexibility in serving packets before beingdemanded, and since the corresponding buffer dynamics are expected to befundamentally different from traditional queues. The latter deviation isbecause the packets in predictive queues can be opportunisticallydropped to increase the chances of future service. This calls for newmeans to measure the buffer content values to exploit such dropping andreplacement opportunities. Various metrics may be incorporated into theoptimization-based design framework.

Utilization of Innovative Transmission Techniques for EfficientMulti-Casting: Wireless communication yields a natural opportunity forefficient dissemination of common data to a number of interested users.Together with innovative coding and transmission techniques, the gainsof wireless broadcast capabilities can be significant. For example,random network coding can yield orders of magnitude throughput and delaygains when used to broadcast common data to multiple users over fadingchannels. This setup is particularly attractive for modeling cellulardownlink scenarios (as in FIG. 2), where much of predictive operation ispossible. Yet, in the absence of the predictive mechanism, data ofinterest to multiple users is requested separately and must bere-transmitted multiple times.

Fortunately, the accurate prediction of future user requests enables thealignment of the data demands of users to exploit wireless broadcastinggains while preserving their deadline requirements. The fundamentaltradeoff is whether to multicast the content to the currently interestedusers, or to wait to increase the multi-cast group size for a latertransmission with greater gains. This question may be addressed in acontrol theoretic framework to optimally pack groups of demands of thesame data for efficient multi-casting which minimizes the relevant costmetrics, such as failure probability, energy consumption, etc. Thisproblem is considered in the context of downlink fading channels, underprediction randomness and errors, and for static and dynamic datasources.

Real-time Pricing for Beneficial Regulation of User Behavior: User's maybe dynamically charged for information content based on its availabilityat the user buffer and the existing network load in order to betterutilize network resources. Accordingly, users with less strict demandscan be encouraged to utilize their already-downloaded content ratherthan download new data, and let the users with more exact demandsconsume the expensive resources of the system. This problem may beapproached from an optimal control point-of-view by specifying the costand pricing structure and by developing provably good policies throughstochastic control and optimization methods.

Beyond Prediction: A Behavioral Science Approach. The embedded structurein the user traffic request patterns may be identified and then utilizedwith the corresponding predictive ability to enhance the efficiency ofspectrum utilization. This paradigm may be extended by relying on theproactive network to influence the user behavior to achieve furthercapacity gains, while preserving, if not improving, the user experience.A behavioral science approach may be used for: 1) incentivizingfavorable user behavior; and 2) assessing the efficiency of the methods.For example, based on the available user profile, the smart device maydecide to download a certain viral video, during the network off-peak,in anticipation that it will be of interest to the user in the future.The user may use the smart device later to search for some media contentof the same genre as the previously downloaded viral. Then, if thecached viral appears on the top of the search outcome list, coupled withthe user's knowing that this choice costs less, then the user may beinclined to use this one instead of downloading another.

The user's willingness to be subject to influence with respect tocontent requests is suggested by work from the uses and gratificationsperspective, which emphasizes the role of specific motivations such asneed for entertainment or information in selections of communicationsmedia. But it is also reasonable to assume that content seekers aremotivated by convenience and economic utility. Uses and gratificationsassociated with the web have been found to vary along such dimensions asentertainment, information, and social interaction, but convenience isan important factor. Kinnally et al. [2] found that among the factorsaccounting for college students' gratifications from music downloading,entertainment/passing the time was the strongest factor, followed byconvenience/economic utility. Not unexpectedly, the latter factor wasalso found to be significantly and negatively associated with their CDpurchases. The ability to sample songs and acquire single songs ratherthan whole albums is frequently cited, clearly economic gratificationsfrom downloading. Cunningham and Nichols [3], in their study based onnaturalistic observations of how people find video on the web, foundthat most sessions began by searching for a particular video, but thatif it was not found rather quickly, a new search was begun. The primarypurpose of the initial search seemed to be to whittle down the vast poolof potentially suitable videos to a more parsimonious set which couldthen be browsed or which could lead to interesting meandering downrelated links. Browsing was a common strategy once the ‘starter’ videowas identified, or if there was a directory with useful categoriesavailable. These findings suggest that even though a user is initiallymotivated to locate a specific file, a primary motivation is to beentertained, a motivation which can be satisfied with other related,recommended content, particularly when it is easily accessible andoffers greater economic utility. Recommendations obtained from a trustedsocial network, or from a system which extends the user's horizonsthrough topic diversification may be particularly welcomed.

One issue that may arise is determination of each user's optimalthreshold for the loss of privacy associated with customization. Clearlymost users of modern telecommunications are well fit by a“self-as-source” model of interaction with media in which they have cometo expect, and to prefer, a great deal of agency, interactivity, andpersonalization, and have resigned themselves to giving up details oftheir private lives in exchange. How much information users are preparedto divulge has been shown to be highly context-dependent, and isbelieved to be influenced by age. Students involved in a study of amusic recommender system which was based on their own preference historyand profile information they provided were less concerned about privacyif they could frequently check and update personal information, and weregenerally willing to provide information in exchange for costs savingsor convenience [4].

This incentive system can relieve some of the burden on the predictionalgorithm. More specifically, it may be sufficient to predict thefavorite genre to the user instead of the specific media content to berequested in the future. To offer a strong incentive for the user to usethe content downloaded during the off-peak time, it should be clear thatthis choice carries with it some economic gain. A real-time pricingframework may serve as an efficient tool to reduce the peak to averagetraffic demand. The efficient design of such a mechanism, however,requires the joint optimization of the resource allocation policies.

The acceptance of this approach by the public may depend critically onthe careful consideration of privacy issues. In other words, makingrecommendations that match network interests may make users uncertainabout their privacy protection. Therefore, to enable wide acceptance ofthe technology, a clearer understanding of the underlying fundamentaltradeoff between economic gain and privacy is developed. Thisunderstanding is expected to be instrumental in the design of arecommendation system that maximizes the capacity of the network, whileoffering an enhanced user experience.

The Spatial Dimension: P2P Overlay over Cellular Networks

Conventional cellular and ad-hoc network architectures are intrinsicallylimited by fundamental bottlenecks that follow from basic informationtheoretic facts. It is well-known that the sum-throughput of a singlecell downlink, with M antennas and K>>M single-antenna users, scales atbest as 0(M log log K) for large K and fixed average receivedSignal-to-Noise Ratio (SNR). This scaling assumes ideal channel stateinformation, and it incurs a degradation when the cost of estimating thefading channel state is taken into account. For this reason, even theso-called Network MIMO architectures are not envisioned to increase thepre-log factor M such that it can scale linearly with the number ofusers K. In order to break the cellular bottleneck, one may opt for aninfrastructure-less ad-hoc network. Hierarchical cooperation or a schemeexploiting large-scale user mobility may be used in order to recover aconstant throughput per source-destination pair. However, hierarchicalcooperation is very complicated, requiring a very tight coordinationbetween the users that must operate as a distributed antenna array at alarger and larger scale. It is not surprising, therefore, that noexisting ad-hoc network makes actual use of such scheme as of yet. Also,a two-hop scheme that exploits mobility incurs a very significant delay0(K log K) for the most common Brownian or simple random walks mobilitymodels. This motivates a proactive P2P wireless overlaying that achievessignificantly better performance than both pure cellular and pure ad-hocnetworks in the context of predictive networks advocated in thisproposal.

The Case for Proactive P2P Overlays

As mentioned above, the accurate prediction of future user requestsenables the alignment of the data demands of users to exploit wirelessbroadcasting gains (i.e., multicasting simultaneously and on the samefrequency band to multiple users). A different approach may be employedwhere the same multicasting effect is obtained by disseminatinginformation through the cell by exploiting device-to-device directcommunications.

Because the transport capacity, in (bit×meter/s), is a fundamentallimitation of conventional wireless networks, the new approach aims toincrease the bits by bringing the average distance between users anddesired content from 0(1) to 0(1/√{square root over (K)}) withoutdeploying a massive media content distribution infrastructure on thecellular coverage. Towards this end, the disclosed cellular P2Pframework leverages the fact that wireless cellular networks are rapidlyevolving from the standard cellular paradigm to heterogeneous networksbased on multiple tiers, whereby, beneath a macro-cellular umbrella,device-to-device direct communication are envisioned to become a realityin the near future, More specifically, the approach relies on injectingfresh packets into the network from one or more fixed access points at aconstant rate. These packets are cached into the user devices anddisseminated in the cellular coverage area by Gossip algorithms. Theproactive nature of the scheme ensures that at any time, a particularuser is guaranteed to find, with a high probability, the desired mediacontent in its own cache or in the cache of its neighbors at distance0(1/√{square root over (K)}). Because mostly local communication isrequired here, 0(K) local device-to-device connections can share thesame time-frequency slot resulting in an overall system throughput thatscales linearly with the number of users.

A QUANTITATIVE EXAMPLE

An asymptotic analysis of a simple scenario is provided, supporting theprevious qualitative claim and motivating the formulation of specificresearch problems. Consider a single-cell system with fixed cell-size0(1) and randomly placed K user terminals. The base-station is equippedwith M antennas and at each time slot transmits a data packet to theuser with the largest instantaneous channel gain, according to theHSDPA/Ev-Do scheme currently used in 3G high-data rate downlink. Thiscan be done with spectral efficiency that scales between 0(1) and 0(Mlog log K). When a user receives a new packet, it starts disseminatingit through the cell using local device-to-device communications,according to a Gossip scheme run on the Random Geometric Graph (RGG)induced by the local communication links. Assuming that the physicallayer supports reliable communication at constant rate between terminalsat distance

$O\left( \sqrt{\frac{\log \mspace{11mu} K}{K}} \right)$

The RGG is connected with high probability. Now, the ϵ-disseminationtime T(ϵ) of the Gossip algorithm is defined as the time after which theprobability that any user misses some packets is less than ϵ. Withϵ=1/poly(K), a Gossip algorithm on a RGG in the connectivity regimeachieves

T(ϵ)=0(√{square root over (K log K)})

By injecting new packets at constant rate λ, and using pipelining, thesame delay is achievable for all such packets. In order to see this inan easy and intuitive way, replace the RGG with a square grid (meshnetwork) with nodes at minimum distance 0(1/√{square root over (K)}),and consider injecting the new packets at a single point at one cornerof the square. Packets propagate in waves, in a pipelined way, and eachwave takes 0(√{square root over (K)}) to propagate across the unitsquare. This estimate is tight up to some 0(log K) term, due to theadditional randomness of the RGG.

Based on the system described above, the following distributed cachingscheme is considered. Suppose that the “popular” content has finite sizeL files at any given point in time (e.g., the 100 most popular TV shows,headline news, stock-exchange reports and music videos). The size of thepopular content does not scale with the number of users K, but changesin time at a fixed innovation rate. In particular, assume that any userat any time slot n places an i.i.d. individual demand q_(k)(n)∈{0, 1, .. . , L, L+1}, where q_(k)(n) corresponds to no request, q_(k)(n)=

∈{1, . . . L} corresponds to requesting packet

already present in the system, and q_(k)(n)=L+1 corresponds torequesting a “new” packet, not present in the system. LetPr(q_(k)(n)=0)=1−p_(old)−p_(new), Pr(q_(k)(n)=

)=p_(old)/L for

∈{1, . . . , L} and Pr(q_(k)(n)=L+1)=p_(new), for some p_(old),p_(old)>0 such that p_(old)+p_(new)≤1. Finally, let Kp_(new)=λ, theconstant rate of innovation, and model the aggregate request of newpackets at a Poisson process, as explained previously. Users cachepackets as they receive them (according to the dissemination processdescribed above), and start discarding the oldest (or least requested)ones when their buffers are full. How to selectively discard or keep“popular” packets is a problem that should be investigated. Assumingthat the user demands can be predicted with T>T(ϵ), the above schemeachieves outage probability ϵ provided that λ is a constant or it doesnot grow faster than 0(M log log K). If a new data connection wasgenerated for each user request, as in today's cellular systems, then adownlink capacity scaling of 0(K) was required, which violates theinformation theoretic limits summarized before. In contrast, in theadvocated system most of the user requests are satisfied by data alreadycached into the network and retrievable by local communication atdistance 0(1/√{square root over (K)}), while the downlink and the Gossipdissemination can take care of the fixed rate of innovation. The aboveexample reveals the natural match between the disclosed proactiveparadigm and the P2P communication overlay on top of the cellulararchitecture.

Device-to-device communication in cellular networks presents severalchallenges. A Base-Station Assisted Device-to-Device (BSA-D2D) approachmay be used in which all the mobiles in the cell are synchronized by thebase station common control channel. This approach allows thepossibility of centralized coordination and centralized networkoptimization, and represents a markedly different paradigm with respectto classical ad-hoc networks. Investigating the design tradeoffs ofBSA-D2D represents a rich and novel domain at the cross-section ofmultiuser information theory, modern coding techniques, and networkoptimization, scheduling and resource allocation.

Prediction models may be expanded to take the spatial dimension intoconsideration. Specifically, it would be beneficial to predict where (orat which P2P network site) information could be also available. Towardsthis end, for each interested data source d, intelligent agents build anavailability model p(y|d, t) that gives the likelihood of obtaining datafrom a P2P participant y at time t. Additionally, the optimalpre-fetching expands the possible actions by the agents to allow for theoption of rerouting a request from the cellular network to a P2P networkat a different time. Such change brings rewards to the agent as itpotentially reduces network traffic. However, the agent is also at therisk of not being able to obtain the content at the presumed P2P siteand if that indeed occurs, a negative reward may be given to the agent.Again, through the appropriate reward-penalty mechanism, the agentlearns the optimal policy that strikes the right balance trade betweentaking opportunistic pre-fetching or relying on the P2P network lateron.

The ϵ-dissemination time T(ϵ) may be improved by modifying the basicGossip algorithm. A natural extension may be as follows: instead ofinjecting new packets to a single user terminal (as done in today's1×Ev-Do, or OFDM/TDMA cellular systems), the base station can multicastthe same packet to a random set of users of linear size 0(K), namely,all users with instantaneous SNR larger than some threshold that dependson the downlink transmission spectral efficiency. Then, instead ofdisseminating the packet from a single point, each requesting user needsonly to reach the random set of users who have received the packet. Thiscan again be done by a Gossip algorithm that propagates queries, in a“pull” scheme (instead of “push”, as in the example). There may be sharptradeoffs of the delay T(ϵ) in this case, in order to determine theprediction diversity gain for this type of P2P architectures.

A very important aspect in P2P networks, in general, concerns thefreeriding and whitewash problems. A freerider is a node that downloadsfrom other nodes but does not want to share, so that it takes advantageof the system without participating in it. Whitewash refers to nodesthat switch identity, so that they can avoid paying the consequences ofmisbehavior. In wireless P2P, the utility of each node is typicallydefined as a weighted difference between the amount of downloaded dataand the amount of uploaded data, where the coefficients reflect theapplication-specific gain related to downloading desired data, and thecost (typically battery energy) of transmitting data to some other user.In these terms, a generalized prisoner's dilemma is present, whosenon-cooperative (single-shot game) Nash equilibrium is no cooperation(zero uploaded and downloaded data). Using the repeated game framework,all points on the Pareto boundary of the payoff region that are strictlydominated by the non-cooperative Nash equilibrium can be enforced(sub-game perfect equilibria of the repeated game). In particular, howto achieve specific points such as the proportionally fair point or themax-min fair point for the specific wireless P2P network at hand(possibly exploiting the centralized base station control channel) is achallenging problem. Furthermore, equilibria must be robust to cheating.For example, the simple tit-for-tat mechanism is reasonably adequate forBit-Torrent: since the collaboration is happening real-time, if a userstops uploading, the other users cut him/her off creating a distinctincentive for collaboration. For distributed caching games involved inproactive P2P networking, such tit-for-tat mechanisms will not worksince users do not need to download content simultaneously. Therefore,there is a need for memory and reputation in the system. Even keepingtrack of each user's history can be challenging: the local client ofeach user can record and report how much the user is donating, butclients can be easily reprogrammed to lie about user contributions.Similar problems are encountered in distributed cloud storage systemspointing towards the need for decentralized Gossip-style algorithms tomaintain reputations, where each user reports how much otherscontributed to her/his downloads enabling the aggregation of reputationacross the network via repeated message passing. Gossip algorithms havebeen proposed for the distributed computation of a function (e.g., theaverage) of local data. In this case, Gossip algorithms can be used tocompute in a decentralized manner a reputation score for all users. Inaddition, if this reputation score takes into account the history length(e.g., as in the FICO Credit Score system), whitewash isdisincentivized.

In this respect, the existing social network structure connecting theusers in cyberspace, well beyond the topology constraints of the randomgeometric graph of the wireless network, is a resource that can beleveraged. In fact, the Gossip-based distributed reputation mechanismcan run on a different time-scale on some (virtual) social networkconnecting the users, while the Gossip algorithm for contentdissemination runs on the graph of the physical wireless network.

Exploiting the Social Structure

A comprehensive approach to understanding how individuals interact withdigital media takes into account the many interconnecting subsystems inthe complex ecology that governs user behaviors, including social ties,the community and geospatial context, technological affordances, and thepolicy environment. A factor which cuts across many of these subsystemsis the individual's social capital: the access to information,connections, and resources made available through an extended network ofcontacts. Social capital is determined not only by the availability ofnetworks of personal friends and family, but also through havingrecourse to so-called bridging social capital: the ties created byfriends of friends and networks of others with common interests provideexposure to information, and resources for the formation of newrelationships. Computer-mediated social networks provide not only ameans to maintain existing relationships but also allow their users toavail themselves of the collective resources of very large numbers ofpeople, most of whom they will never meet face to face. Socialnetworking and location-based technologies may be incorporated into theP2P content application to incentivize cooperation across the networkvia offering the added benefit of extending the cooperating userresource networks and social capital.

Each individual user may be viewed as the nexus of a networked communityor interest space, that is, a virtual network loosely coupled to similarspaces organized around other users and their peers. The networkprovides the user ready access to external resources as the ties createdthrough intersecting circles of user interest spaces provide exposure tonew contacts, information, and resources.

To deploy and evaluate an opportunistic peer-to-peer distributionsystem, online content is routed through the network via local wirelessconnections, smoothing out and shifting the burden of demand to off-peakhours and locally proximate content providers. Each participant has atwo-tiered network of peers, the first composed of peers from within theuser's existing online social networks, and the second tier frompresumably unknown but geographically co-located users. Layered on topof these tiers is a location-based application that links users witheach other, the places they visit, and the resources they possess. Auser seeking particular online content is able to create defaultsettings that request the desired content from an existing socialnetwork member first, or from closely proximate cohort members, or somecombination thereof. Users are able to provide reputation ratings anduse the application to add members from the cohort to their onlinesocial networks. Of particular interest is whether the intersection ofuncommon (infrequently requested) content interests and geospatiallocation results over time in more persistent connections among userswho are not initially included in each other's social networks. That is,repeatedly obtaining files from a geographically co-located butpreviously unknown source may provide a sufficient basis for addition ofthe source to one's social network. Tagging of file-sharing pairs andfollowing them over time assists in determining if file sharing requestsincrease or if other types of connection are formalized.

Incentivizing Use: The more frequently the P2P application is used, themore rapidly and successfully online content can be transferred and themore quickly users' resource requesting behavior can be learned tofacilitate provision of the desired content at the right time and place.Incentivizing users to install and use a P2P application for requestingnetwork resources may involve a combination of factors above and beyondwhatever contribution to the collective good their participation maymake. Material rewards such as text-messaged coupons for discounts,iTunes credit or other digital goods can be effective. An applicationmay have greater appeal to users if they experience a feeling ofinhabiting a shared space with others, and their awareness of mediationby technology recedes into the background.

Measuring Usability and Acceptance: The P2P system may be assessed andimproved through the use of cell-phone based ecological momentaryassessments and interventions. (EMA/I). EMA/I is a set of strategies forcollecting pre-scheduled as well as unannounced assessments anddeploying interventions in people's naturally occurring environments.Measurement biases associated with recall can be avoided, and messagesresponding to events can be tailored dynamically as they occur.Intervention and data collection are location-independent, administeredthrough familiar devices likely to be at hand. In an example embodiment,the instrument of intervention delivery and assessment are the same—theuser's mobile phone. This method may be used to administer periodicusability surveys focusing on perceived usefulness and ease of use andto collect additional data on social influence, facilitating conditions,and intent to continue using the application. For administering ofquestionnaires, user information may be protected by requiringtwo-factor SMS user authentication. Secure hard opt-in from users may berequired each time they receive a request for an assessment.

The disclosed predictive network is responsive to one of the mostpressing communications issues of today: spectrum capacity. The wirelessspectrum is a limited and nonrenewable resource which is increasinglychallenged by the growing demand for service created by the widespreadavailability of smartphones and other devices connecting to the internetthrough cellular networks. Some wireless providers have responded to thechallenge not only with plans for building new or expanding existinginfrastructure and lobbying for more spectrum, but by dropping unlimiteddata plans for new subscribers in the hope of encouraging consumers totake advantage of available Wi-Fi access. Technological challenges whichare addressed by shifting the costs of meeting them to consumers mayhave the unintended consequence of fueling a digital divide alongeconomic lines. The disclosed predictive network system and methodfocuses on maximizing available spectrum in order to preserveinexpensive access for all, not only to the informational resources ofthe internet but to newer and throughput-hungry applications whichbenefit consumers like mobile VoIP, location-based services, andhealthcare diagnosis and monitoring. The disclosed system and methodprovides a simple and efficient response to the capacity issue thatleverages the heretofore untapped storage and memory potential of themillions of handsets in use, to smooth out demand and shift traffic frompeak to off-peak time by proactive response to predictable user demandsand exploitation of cache memory.

With an agent installed on the user's mobile device or at a server,machine learning techniques train on the user's content request historyand create innovative techniques for learning from dynamically generatedcontent. The agent learns what content the user wants, where she wantsit, and when, and coordinates with other agents on other devices on thenetwork to effect proactive retrieval of content. The system and methodleverages commonalities of content preference, location, and requesttiming to maximize the availability of desired content on the user's owndevice and minimize demand on the network. It overlays the physicalnetwork with a social network structure used to maintain reputations andadminister incentives for cooperative behavior. It delivers thecomponent technologies needed to effect a fundamental change in the wayusers' mobile content demands are fulfilled, without reducing thequality of service. The predictive/proactive network takes thehuman/user factor as a key component of a “feedback control loop” (in abroad sense), instead of modeling users as unpredictable/memorylessrandom request generators.

Therefore, while certain embodiments of the present invention aredescribed in detail above, it is to be understood that the scope of theinvention is not to be considered limited by such disclosure, andmodifications are possible without departing from the spirit of theinvention as evidenced by the following claims:

1. A method comprising: collecting, from a network, a plurality of network content requests; comparing the plurality of network content requests against a statistical model to predict a plurality of anticipated network content requests, wherein the statistical model is based at least in part on network content requests from a plurality of mobile devices, and wherein the plurality of anticipated network content requests comprises a first data source and a second data source; and serving network content responsive to one or more of the anticipated network content requests from a source selected from a group consisting of the first data source, the second data source, and a combination thereof, wherein the serving occurs based on, in part, a comparison between a network supply and a network demand to ensure an optimal utilization of available network bandwidth.
 2. The method of claim 1, wherein the list of anticipated requests further comprises a deadline for each anticipated network content request and wherein the network content is served before the deadline.
 3. The method of claim 2, wherein the deadline is a time.
 4. The method of claim 1, wherein the plurality of network content requests are collected at least in part from a mobile device connected to the network.
 5. The method of claim 4, wherein the network content is served to the mobile device via the network.
 6. The method of claim 5, wherein the network content is configured to be stored in a local memory at the mobile device, thereby being available for display on the mobile device upon a user's request.
 7. The method of claim 4, further comprising detecting, via the mobile device, a wireless network.
 8. The method of claim 7, wherein the detecting the second network comprises receiving a location of the mobile device.
 9. The method of claim 8, wherein the location indicates that the mobile user device is within range of a wireless network.
 10. The method of claim 1, wherein the method is performed at least in part by a server connected to the network.
 11. A system comprising: a processor; and a memory storing instructions which, when executed by the processor, cause the processor to: collect, from a network, a plurality of network content requests; compare the plurality of network content requests against a statistical model to predict a plurality of anticipated network content requests, wherein the statistical model is based at least in part on network content requests from a plurality of mobile devices, and wherein the plurality of anticipated network content requests comprises a first data source and a second data source; and serve network content responsive to one or more of the anticipated network content requests from a source selected from a group consisting of the first data source, the second data source, and a combination thereof, wherein the serving occurs based on, in part, a comparison between a network supply and a network demand to ensure an optimal utilization of available network bandwidth.
 12. The system of claim 11, wherein the list of anticipated requests further comprises a deadline for each anticipated network content request and wherein the network content is served before the deadline.
 13. The system of claim 12, wherein the deadline is a time.
 14. The system of claim 11, wherein the plurality of network content requests are collected at least in part from a mobile device connected to the network.
 15. The system of claim 14, wherein the network content is served to the mobile device via the network.
 16. The system of claim 15, wherein the network content is configured to be stored in a local memory at the mobile device, thereby being available for display on the mobile device upon a user's request.
 17. The system of claim 14, further comprising detecting, via the mobile device, a wireless network.
 18. The system of claim 17, wherein the detecting the second network comprises receiving a location of the mobile device.
 19. The system of claim 18, wherein the location indicates that the mobile user device is within range of a wireless network.
 20. A method comprising: collecting, from a network, a plurality of network content requests; comparing the plurality of network content requests against a statistical model to predict a plurality of anticipated network content requests, wherein the statistical model is based at least in part on network content requests from a plurality of mobile devices, and wherein the plurality of anticipated network content requests comprises a first data source and a second data source; serving network content responsive to one or more of the anticipated network content requests from a source selected from a group consisting of the first data source, the second data source, and a combination thereof, wherein the serving occurs based on, in part, a comparison between a network supply and a network demand to ensure an optimal utilization of available network bandwidth; receiving at least one actual request for content; and fulfilling the at least one actual request using the served content. 